search for: mealscombin

Thanks Boris , this was very helpful but I'm struggling with the last part. 1) I combined the first 2 columns :- library(tidyr) SingleMealsCode <-unite(MyData, MealsCombinations, c(MealA, MealB), remove=FALSE) SingleMealsCode <- SingleMealsCode[,-2] 2) I separated this dataframe into different dataframes based on "MealsCombination" column so R will recognize each meal combination separately : SeparatedGroupsofmealsCombs <- split(SingleMealCode,...

...! and this is only for for one dataframe in the list .. I have around 150-200 dataframes.. not sure how R will store these results.. but first I need the analysis to be done correctly. The final output has to be something like this :- > mafsforeachcolumns(I,II,...600)foreachcombination MealsCombinations Cust.ID I II III IV ...... 600 1 33-55 1 0.124 0.10 0.65 0.467 3 5 2 44-66...

...gt; for one dataframe in the list .. I have around 150-200 dataframes.. not sure how R will store these results.. but first I need the analysis to be done correctly. The final output has to be something like this :- > > >> mafsforeachcolumns(I,II,...600)foreachcombination > > MealsCombinations Cust.ID I II III IV ...... 600 > 1 33-55 1 0.124 0.10 0.65 0.467 > 3 > 5 &...

...for one dataframe in the list .. I have around 150-200 dataframes.. not sure how R will store these results.. but first I need the analysis to be done correctly. The final output has to be something like this :- > > >> mafsforeachcolumns(I,II,...600)foreachcombination > > MealsCombinations Cust.ID I II III IV ...... 600 > 1 33-55 1 0.124 0.10 0.65 0.467 > 3 > 5 &...

...On Nov 17, 2017, at 7:15 PM, Allaisone 1 <allaisone1 at hotmail.com> wrote: > > > Thanks Boris , this was very helpful but I'm struggling with the last part. > > 1) I combined the first 2 columns :- > > > library(tidyr) > SingleMealsCode <-unite(MyData, MealsCombinations, c(MealA, MealB), remove=FALSE) > SingleMealsCode <- SingleMealsCode[,-2] > > 2) I separated this dataframe into different dataframes based on "MealsCombination" > column so R will recognize each meal combination separately : > > SeparatedGroupsofmealsCom...

...ric argument to binary operator I think this can be solved by converting columns (I,II,II,..600) into "numeric" instead of the current "int" type as shown below in the structure of "33_55" dataframe . $ 33_55:'data.frame': 256 obs. of 600 variables: ..$ MealsCombinations : chr [1:256] "33_55" "33_55" .... ..$ ID : num [1:256] 1 3 5 ... ..$ I : int [1:256] 1 1 2 1 1 2 1 2 1 1 ... ..$ II : int [1:256] 2 1 2 2 1 2 2 2 2 2 ... ..$ III...

...in the list .. I have around 150-200 dataframes.. not sure how R will store these results.. but first I need the analysis to be done correctly. The final output has to be something like this :- >> >> >>> mafsforeachcolumns(I,II,...600)foreachcombination >> >> MealsCombinations Cust.ID I II III IV ...... 600 >> 1 33-55 1 0.124 0.10 0.65 0.467 >> 3 >>...

Combine columns 1 and 2 into a column with a single ID like "33.55", "44.66" and use split() on these IDs to break up your dataset. Iterate over the list of data frames split() returns. B. > On Nov 17, 2017, at 12:59 PM, Allaisone 1 <allaisone1 at hotmail.com> wrote: > > > Hi all .., > > > I have a large dataset of around 600,000 rows and 600

...list .. I have around 150-200 dataframes.. not sure how R will store these results.. but first I need the analysis to be done correctly. The final output has to be something like this :- > > > > > >> mafsforeachcolumns(I,II,...600)foreachcombination > > > > MealsCombinations Cust.ID I II III IV ...... 600 > > 1 33-55 1 0.124 0.10 0.65 0.467 > > 3 > >...

Hi all .., I have a large dataset of around 600,000 rows and 600 columns. The first col is codes for Meal A, the second columns is codes for Meal B. The third column is customers IDs where each customer had a combination of meals. Each column of the rest columns contains values 0,1,or 2. The dataset is organised in a way so that the first group of customers had similar meals combinations, this